From the course: AI Trends

NVIDIA DGX Spark

From the course: AI Trends

Start my 1-month free trial Buy for my team

NVIDIA DGX Spark

“

- [Instructor] This is the NVIDIA DGX Spark, also known as the world's smallest supercomputer. You can use it to run pretty large language models. You can also use the DGX Spark for image and video generation and to power developer tools. Now, local AI development isn't new, but this device offers a few features that were previously pretty hard to come across. First of all, the Spark has a lot of memory to work with, 128 gigabytes of unified memory available to the graphics processing unit. This means you can run pretty large language models, sometimes with up to 200 billion parameters. The device can also be used for fine tuning. Another helpful feature of the Spark is that it leverages NVIDIA's CUDA platform for parallel computing. This gives developers and researchers access to a broad ecosystem of models and fine tuning libraries. The unit comes with a Linux-based operating system, which you can use by plugging in a mouse, keyboard, and monitor. I have a feeling, though, that many developers are going to connect to the Spark using a different computer. This is a very powerful way of working with AI models because it means you can offload a lot of the computational work through the Spark. I can definitely see this becoming useful in industries where there's a great emphasis on data privacy and security. This can also be very useful in AI research and development to supplement cloud compute. Now, setting up the spark does require some technical know-how. This doesn't mean less technical users can't leverage this technology. A less technical team would need some IT support in order to set up the Spark unit and maintain it. Innovations like this can help democratize AI research and development by empowering more individuals and teams to solve more problems with emphasis on data privacy. To learn more about open weight models and local AI development, check out my course on developing with GPT-OSS.

Contents